A lot of AI workflows now end with a wall of text. Useful text, but still text. Examples: research summaries meeting notes study explanations long reports agent task updates internal documentation scripts course material customer research product briefs The model may save you time creating the output, but then you still have to sit there and read everything. That feels like an underrated bottleneck. We talk a lot about better models, bigger context windows, agents, tools, memory, and automation. But the output layer still often looks the same: another document, another chat answer, another markdown file, another summary sitting unread. I think audio becomes more interesting here. Not as a replacement for reading, but as a second way to consume AI output: listen to research while walking review long summaries away from the screen turn study notes into audio make internal updates easier to consume convert generated scripts into narration turn private documents into listenable drafts make AI outputs useful during commuting or low-focus tasks I’m building Murmur, a local-first Mac text-to-speech app, because this problem kept showing up for me. Link: https://www.murmurtts.com/ It runs locally on Apple Silicon after setup/model downloads, supports longer scripts, multiple local voice models, voice cloning / voice design depending on the model, and WAV/M4A export. The local part matters because a lot of the text people want to turn into audio is private: notes, docs, client material, drafts, research, internal writing. I’m curious how other people think about this. As AI creates more written output, do you think audio becomes a real interface layer, or will most people keep consuming everything as text? submitted by /u/tarunyadav9761
Originally posted by u/tarunyadav9761 on r/ArtificialInteligence